Why Ragin's fuzzy techniques lead to successful social science applications: an explanation

To find the relation between two concepts, social scientists traditionally look for correlations between the numerical quantities describing these concepts. Sometimes this help, but sometimes, while we are clear that there is a relation, statistical analysis does not show any correlation. Charles Ragin has shown that often, in such situations, we can find statistically significant correlation between the degrees to which experts estimate the corresponding concepts to be applicable to given situations. In this paper, we provide a simple explanation for this empirical success. 1 Ragin’s Approach to Social Research: A Brief Description and Need for Justification Typical social science questions. Based on several observations and/or on some theoretical ideas, a social science researcher formulates a hypothesis that some property A imply some other property B: e.g., that rich countries are usually democracies, or that the socio-economic status of parents affects the success of their children in school. Once the hypothesis is formulated, it needs to be checked against all available data. In some cases, as a result of this check, the hypothesis is validated. In other cases, a detailed data analysis shows that, contrary to anecdotal evidence and/or theoretical ideas, there is no causal relation between the corresponding phenomena A and B. How to check the proposed hypothesis: a traditional correlationbased approach. Traditionally, the hypothesis is checked in the following way. First, we come up with reasonable numerical quantities a and b that describe to what extent properties A and B are satisfied.